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README.md
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---
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license: apache-2.0
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datasets:
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- rajpurkar/squad
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- google-research-datasets/natural_questions
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- hotpotqa/hotpot_qa
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- Project Type: Bring your own method
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## Structure
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- `data/` contains the data used for the project
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- `distilbert.py` contains the code for the DistilBERT model and the Dataset.
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- `distilbert.ipynb` contains the creation and training of the DistilBERT model
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- `distilbert.model` is the distilbert model
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- `distilbert_reuse.model` is the question answering model
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- `load_data.py` contains the code for loading the data and preprocessing it.
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- `qa_model.py` contains the code for thee different QA models.
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- `qa_model.ipynb` contains the creation and training of the QA models.
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- `requirements.txt` contains the requirements for the project
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- `utils.py` contains some helper functions for the project. It contains the functions to evaluate the models and a way to visualise the trained parameters for each model.
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* Target for Error Metric:
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* EM: 0.6
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* F-1: 0.7
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* Achieved value: I almost achieved the target for both of the measurements.
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* EM: 0.52
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* F-1: 0.67
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---
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license: apache-2.0
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datasets:
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- Skylion007/openwebtext
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- rajpurkar/squad
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- google-research-datasets/natural_questions
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- hotpotqa/hotpot_qa
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- Project Type: Bring your own method
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## Structure
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+
- `data/` contains the data used for the project
|
16 |
+
- `distilbert.py` contains the code for the DistilBERT model and the Dataset.
|
17 |
- `distilbert.ipynb` contains the creation and training of the DistilBERT model
|
18 |
- `distilbert.model` is the distilbert model
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19 |
- `distilbert_reuse.model` is the question answering model
|
20 |
+
- `load_data.py` contains the code for loading the data and preprocessing it.
|
21 |
+
- `qa_model.py` contains the code for thee different QA models.
|
22 |
- `qa_model.ipynb` contains the creation and training of the QA models.
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- `requirements.txt` contains the requirements for the project
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- `utils.py` contains some helper functions for the project. It contains the functions to evaluate the models and a way to visualise the trained parameters for each model.
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* Target for Error Metric:
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* EM: 0.6
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* F-1: 0.7
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67 |
+
* Achieved value: I almost achieved the target for both of the measurements.
|
68 |
* EM: 0.52
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69 |
* F-1: 0.67
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